Object detection is the task of localizing and classifying objects of interest in an image. To mention a few, Faster R-CNN [1]} and YOLO [2]} are two CNN-based early models, successfully demonstrating deep learning's capability in the object detection task. Recently, following the success of transformers [3]} in natural language processing, DETR [4]} adopts transformers in the object detection task, which performs self-attention on CNN features from different sub-regions of an input image to consider their relationships. Furthermore, Deformable DETR [5]} is proposed to attend only to the relative spatial locations when performing self-attention rather than considering all locations, which makes the transformer-based prediction architecture efficient. However, despite their huge improvements in regard to developing new object detection networks and their performances, they largely overlook the relationships among objects. In particular, CNN-based models rely on constructing hierarchical structures of local and global features for capturing feature-level relationships via multi-level feature map, which is known as a feature pyramid network (FPN) [6]}, but not capturing the explicit object-level relationships. Meanwhile, transformer-based models only implicitly consider relationships between candidate queries for objects, but also candidate queries often include duplicated objects or meaningless background areas which particularly belong to 'no object'. Thus, unlike these previous works, we propose to explicitly model object-level relationships, by initially detecting objects and then, over the graph structure with objects as nodes, representing them with graph neural networks (GNNs) for obtaining graphical features, which are then combined with CNN features for final object detection.
